Summary of Practical Considerations For Agentic Llm Systems, by Chris Sypherd et al.
Practical Considerations for Agentic LLM Systems
by Chris Sypherd, Vaishak Belle
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper focuses on using Large Language Models (LLMs) as the underlying models for autonomous agents. Although LLMs demonstrate emergent abilities and broad expertise across natural language domains, their unpredictability makes implementing LLM agents challenging. To bridge this gap, the authors frame actionable insights from the research community in established application paradigms to enable construction and informed deployment of robust LLM agents. The paper positions relevant research findings into four categories: Planning, Memory, Tools, and Control Flow, based on common practices in application-focused literature. It highlights practical considerations for designing agentic LLMs, such as handling stochasticity and managing resources efficiently. This work provides necessary background for discussing critical aspects of agentic LLM designs in academia and industry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using powerful language models to help create autonomous agents that can make decisions on their own. These language models are very good at understanding and generating human language, but they’re also unpredictable, which makes it hard to use them for real-world applications. The authors want to fill the gap between what we know theoretically and what’s possible in practice by providing a framework for building and deploying these autonomous agents. They highlight four important areas to consider: planning, memory, tools, and control flow, as well as practical challenges like managing resources and dealing with uncertainty. |
